1

Surveillance Plan for High Pathogenic 

Avian Influenza in Wild Migratory 

Birds in the United States

May 20th, 2017

NATIONAL FLYWAY COUNCIL

Pacific est. 1952 - Central est. 1948 - Mississippi est. 1952 - Atlantic est. 1952

Photo courtesy of Nicole Rein

Jay Cumbee
Surveillance Plan for HPAI in Wild Birds

2

Table of Contents

Table of Contents...............................................................................................................................2

Introduction .......................................................................................................................................3

High Pathogenic Avian Influenza in the United States.......................................................................3

Specific Goals and Objectives ............................................................................................................3

Migratory Waterfowl Movement ......................................................................................................4

Species Identified for Collection ........................................................................................................5

Identification of Priority Watersheds.................................................................................................5

Sample Size Estimation ................................................................................................................................8

Sample Collection.............................................................................................................................12

Appendix 1. Wild Bird Species That Tested Positive for HPAI in the United States (December 2014-

February 2017)...............................................................................................................................133

Appendix 2. Methods for Historical AI Clusters in Wild Birds..........................................................13

Appendix 3. Development of Watershed Level Consolidation Factor (Waterfowl Mixing) 

Metrics...........................................................................................................................................144

Appendix 4. Dabbling Duck Population Estimation and Temperature Constraints.......................177

Appendix 5. Morbidity and Mortality Surveillance........................................................................200

Appendix 6. Wildlife Conservation Concerns ......................................................................................... 21

Appendix 7. Poultry Production Regions.......................................................................................222

References .....................................................................................................................................244

LE OF CONTENTS

Photo courtesy of Jay Cumbee
Surveillance Plan for HPAI in Wild Birds

3

Introduction

This document describes a plan to conduct national level surveillance for high pathogenic avian 

influenza virus (HPAI) in wild migratory birds. Collaborating entities include the USDA Animal and 

Plant Health Inspection Service (APHIS) Wildlife Services (WS) and Veterinary Services (VS); the 

United States Geological Survey (USGS); U.S. Fish and Wildlife Service (USFWS); and the National 

Flyway Council. This surveillance plan supplies detailed methods on the reasoning behind, and the 

development of, wild bird avian influenza surveillance. The Implementation Plan for High Pathogenic 

Avian Influenza (HPAI) Surveillance in the United States is a more concise supporting document that 

provides sample number targets for each state. Implementation of national level surveillance 

directly supports the U.S. Interagency Strategic Plan for Monitoring Influenza A Viruses of 

Significance in Wild Birds (Interagency Strategic Plan).

High Pathogenic Avian Influenza in the United States

The first report of clade 2.3.4.4 H5N1 HPAI occurred in Asia in 1996 and now a diverse viral gene 

pool exists in the world due to co-circulation of these avian influenza A viruses (AIVs) and others in 

domestic and wild birds. The Asian clade 2.3.4.4 H5N1 HPAI is the predecessor of multiple viral 

reassortants, including H5N2, H5N5, and H5N8. 

In the fall of 2014, Eurasian clade 2.3.4.4 HPAI H5N2 was identified in commercial poultry in the 

Fraser Valley region of southern British Columbia, Canada. Subsequent samples collected from wild 

birds in the United States, combined with mortality events associated with captive raptors, revealed 

a least three Eurasian H5 HPAIs in circulation. Between March and June of 2015, HPAI outbreaks in 

US domestic poultry operations resulted in the loss of nearly 50 million birds. HPAI has been isolated 

from wild birds in the USA in 2016 and 2017 as part of this surveillance effort, demonstrating that 

Eurasian clade 2.3.4.4 viruses continue to circulate in the USA and still pose a threat to domestic 

poultry. High pathogenic avian influenza viruses are also emerging in other parts of the world and 

are, as of May 2017, causing unprecedented losses in European domestic poultry, illustrating the 

continued potential for other novel HPAI introductions into the United States. Federal and State 

agencies continue to collect wild bird samples to define the extent of HPAI infection in specific avian

species groups.

Specific Objectives

The purpose of this document is to detail the essential methods behind a national surveillance 

system for influenza viruses of interest in wild, migratory birds. This plan is intended to provide 

guidance for Federal agencies and other cooperators to conduct influenza surveillance in dabbling 

ducks, which are the avian group most likely to be infected with influenza viruses. This focus 

increases our chances of detecting AI viruses and used wild birds as sentinels in a real-time, early 

warning system, which poultry producers and others can use to guide their management decisions.

Data generated through this surveillance effort will provide information that can improve 

management actions that are taken to address the multitude of issues associated with HPAI. These
Surveillance Plan for HPAI in Wild Birds

4

risks include infections in commercial poultry, backyard poultry, game bird farms, wild birds, wild 

bird rehabilitation facilities, falconry birds and captive bird collections in zoos and aviaries. 

Objectives

1) Identify the distribution of HPAI viruses of interest (currently clade 2.3.4.4 viruses) by U.S. flyways 

and through select, high priority watersheds

2) Detect spread of HPAI viruses of interest to new areas of concern

3) Provide a flexible surveillance framework that can be modified to monitor wild waterfowl 

populations for novel reassortant influenza A viruses and for introduction of new influenza A viruses, 

4) To sequence influenza viruses from wild birds to better understand pathways that lead to 

outbreaks in commercial and backyard poultry

Migratory Waterfowl

Movement

Waterfowl and water bird 

migration in North America 

generally consists of northsouth seasonal movements 

between breeding grounds and 

wintering areas. There are four 

major flyways in North America 

(Figure 1). These flyways are 

broadly defined corridors where 

the migratory paths of many 

species of interest tend to 

converge. They are associated 

with major topographical 

features in North America, 

which are generally aligned 

along a north-south axis. The 

four flyways—Atlantic, Mississippi, Central, and Pacific—have areas of overlap and convergence, 

particularly at the north and south ends. The flyway boundaries are defined administratively, and 

are not biologically fixed or sharply defined. 

North American flyways represent the predominant pathways of migratory bird movements within 

broad geographic areas. Many migratory bird species use specific flyways; however, many

individuals within species migrate across flyways during the fall and spring. The Pacific Flyway is

thought to be the most likely area of introduction for the HPAI viruses detected in Canada and the 

United States in December of 2014. 

Figure 1: The four primary waterfowl flyways

Figure 1: The four primary waterfowl flyways
Surveillance Plan for HPAI in Wild Birds

5

Species Identified for Collection

In 2006, the U.S. Departments of Agriculture and Interior, along with multiple State and tribal 

agencies, implemented a nationally coordinated avian influenza surveillance effort in wild birds. This 

large-scale surveillance system has provided an unprecedented amount of data on avian influenza 

viruses in wild bird populations and has informed current surveillance efforts. This dataset, along 

with many others, has identified dabbling ducks as the primary reservoir for avian influenza viruses, 

including H5s and H7s. Therefore, the primary focus of sampling will continue to be on these species 

(Genus Anas, Aix, Cairina, and Dendrocygna): American green-winged teal, mallard, northern pintail, 

American black duck, wood duck, blue-winged teal, cinnamon teal, northern shoveler, gadwall, 

Muscovy duck, mottled duck, and American wigeon. The fulvous whistling duck is not taxonomically 

a dabbling duck but because of its foraging habits it is also included in the same functional group for 

the purposes of this surveillance plan. 

Identification of Priority Watersheds

Watersheds were selected as the sampling unit for the surveillance design, with the intended goal of 

using a biologically relevant scale to sample wild waterfowl and their influenza A viruses.

Watersheds are used to identify key factors important to waterfowl and influenza biology and 

ecology. To identify the baseline HPAI distribution across U.S. flyways and constituent watersheds 

and to detect early spread of HPAI to new flyways and regions, sample units were defined 

geographically using the USGS 4-digit hydrologic unit code (HUC4). There are approximately 222

HUC4s in the United States and they represent an area drained by a river system, a reach of a river 

and its tributaries in that reach, a closed basin(s), or a group of streams forming a coastal drainage 

area [1]. 

To identify which watersheds increase the likelihood of detection for influenza viruses of interest

and are of agricultural importance, we used a combination of three measures and two constraints to

capture the underlying biological aspects for influenza. The measures included: 1) watersheds with 

significant historic influenza clusters, 2) watersheds identified as having high inter- and intra-flyway 

mixing of dabbling ducks within the lower 48 States and 3), watersheds with high numbers of 

domestic chickens and turkey. The two constraints were based on median annual number of days 

above 0°C and below 0°C in a watershed. These were used in concert to develop targeted 

surveillance. Hawaii was not included in this analysis, because of significant differences in native 

avifauna
Surveillance Plan for HPAI in Wild Birds

6

Figure 2. Targeted watersheds that ranked as a significant priority (colored) or low 

priority (gray/white) using network movements of dabbling ducks, influenza A clusters, 

and commercial poultry concentrations.

A) Targeted

Watersheds were 

further prioritized 

by identifying 

watersheds in the 

upper 50th

percentile of 

dabbling duck 

network mixing 

importance or 

with a historically 

significant 

influenza A 

cluster.

Commercial 

poultry 

concentrations 

were also 

incorporated.

Sampling varies 

seasonally.

1) Historic influenza clusters

To identify watersheds with significant influenza A clusters, we used LPAI data collected from 

dabbling ducks during the previous avian influenza surveillance effort to identify priority samplecollection areas. While using LPAI data is not ideal, the HPAI viruses circulating in the US during 

2014/2015 did not cause large scale morbidity and mortality in dabbling ducks, and in that respect 

behaved similarly to LPAI viruses. These data encompass known seasonal differences in influenza 

prevalence in waterfowl and important biological periods for waterfowl migration, both linked to 

influenza dynamics. The analysis considers three periods. These periods represent the summer 

breeding season (May-August), fall migratory season (August-December), and the over-wintering 

season (December-February). The spring northern migration was not included for three reasons.

First, data to estimate spring populations is not available. Second, data describing spring influenza is 

limited. Finally, logistical constraints on sampling during this time of year are significant. 

All dabbling duck samples tested for the influenza A gene were separated by the three biologically 

relevant seasons and analyzed using the Getis-Ord Gi* spatial statistic to identify historic “hot spots” 

of influenza activity in dabbling ducks. This resulted in identifying specific watersheds as having or 
Surveillance Plan for HPAI in Wild Birds

7

not having significant influenza clusters (see Appendix 2). This analysis revealed geographic regions 

where influenza virus in sampled waterfowl was higher than expected [2]. A majority of these 

influenza clusters occur in more northern latitudes, a finding supported by previous studies related 

to location of major waterfowl breeding grounds and additional research on the environmental

persistence of influenza viruses at cold temperatures [3]. These results offer an important data 

stream to identify high priority areas for wild bird avian influenza surveillance. 

2) Inter and intra-flyway mixing of dabbling ducks

To identify watersheds important for mixing 

of waterfowl populations, we used the 

consolidation factor (CF), a recently 

developed metric that pinpoints geographic 

areas of high mixing for multiple dabbling 

duck species [4] . Specifically, this metric is a 

measure of within- and between-flyway 

movement processes important for 

influenza, and is based on historic U.S. Fish 

and Wildlife Service banding data. The CF

was used to identify watersheds within each 

flyway that account for the majority of 

within-flyway mixing of waterfowl and to 

identify watersheds important for linking 

watersheds (identifying watersheds where 

mixing of birds from multiple flyways occurs.)

See Appendix 3 for detailed methods. 

3) Commercial poultry production

To ensure that surveillance encompassed areas with substantial domestic poultry production, 

watersheds were identified that contained an estimated annual average inventory greater than 10 

million birds. These data were generated by the Farm Location and Animal Population Simulator 

(FLAPs) [5] and include all chicken and turkey production in the US

(http://flaps.biology.colostate.edu). The FLAPs data are generated using a probabilistic model that 

represents the likelihood of farm presence based on 2012 National Agricultural Statistics Service 

data. Watersheds with more than 10 million birds were used in concert with historic avian influenza 

clusters and with areas with high inter and intra-flyway mixing of dabbling ducks to identify which 

watersheds to target for avian influenza surveillance in wild birds. 

4) Temperature constraints

Figure 3. Example map showing nodes that 

depict the spatial distribution of mallards. 

Color represents flyway membership and the 

size of the node is scale to the CF metric, 

representing regions important to flyway 

structure and thus potential areas of 

increased mixing. Filled circles are 

statistically significant CF nodes at αα=0.05. 
Surveillance Plan for HPAI in Wild Birds

8

Targeted priority watersheds were then constrained to address biological processes for both the 

host and pathogen. Temperature is associated with waterfowl migration and influences avian 

influenza persistence in the environment. There is a well-documented pattern of reduced avian 

influenza prevalence in waterfowl at southern latitudes, especially during the breeding season [3]. 

This is largely attributed to viral degradation at higher temperatures [6]. To address this within the 

framework of a targeted surveillance effort, we incorporated the documented decay of virus at 

increasing temperatures above 0 ̊C to identify watersheds during the breeding period in which AI 

was less likely to persist. Because water salinity and pH also influence avian influenza, we used a 

conservative approach—removing watersheds with a 30-year median annual number of days above 

0 ̊C of 365. See Appendix 4 for detailed methods.

Ambient temperature and number of consecutive days below 0 ̊C have direct consequences for 

waterfowl [7,8], and can affect their energy requirements. These cumulative effects also influence 

water temperature and ice formation. Increasing ice coverage can decrease availability of wetland 

foods, reducing nutrient acquisition by wetland-obligate waterfowl (e.g., gadwall, northern 

shoveler) and resulting in migration. We addressed this by removing watersheds from surveillance 

during the overwintering period in which waterfowl were unlikely to be present. Because migration 

can be influenced by a myriad of other factors such as snow and ice cover precluding foraging for

field-feeding waterfowl (e.g., mallard), we removed watersheds with an estimated 30-year median 

number of days below 0 ̊C of at least 180 from the winter surveillance effort.

Sample Size Estimation

1) Waterfowl Population Estimates

Currently, seasonal population estimates for waterfowl at a watershed or regional level are not 

available for the entire United States. To mitigate this, we developed a Bayesian Gamma-Poisson 

Mixture model to estimate a 15-year (1998-2013) median seasonal population within each 

watershed. The approach integrated bird band recovery data and the estimated annual continental 

dabbling duck population available from the U.S. Fish and Wildlife Service. The result was an 

estimate of the median population of ducks (with 95 percent credible intervals) that might be 

expected in the watershed during the fall and winter. We used an alternative method to derive 

breeding season (May-August) population estimates because bird banding data at the continental 

scale is not available for the spring or summer periods. To estimate summer populations, we used 

the breeding bird survey data, one of the only large-scale datasets available, to develop an 

aggregate estimate of relative abundance at the watershed scale [9]. We did not estimate spring 

populations because band recovery data or other survey data are not available. Our approach for 

developing coarse population estimates ensures that the influenza surveillance effort accounts for

the movement of migratory dabbling duck species across space and time. While these approaches 

have limits, they provide a relative understanding of the distribution and potential populations 

across the United States. Appendix 4 describes the method for estimating populations and the 

corresponding assumptions in greater detail. 
Surveillance Plan for HPAI in Wild Birds

9

2) Avian Influenza Occurrence in U.S. Waterfowl Populations

To estimate true seasonal expected influenza A prevalence within watersheds, we used prior 

dabbling duck surveillance data collected from 2006 to 2011 and from 2015-2017. A Bayesian BetaBinomial model was used to estimate the true seasonal and monthly prevalence. The Bayesian 

model accounted for uncertainties in the diagnostic test process (sensitivity and specificity), 

differences in seasonal and monthly sampling efforts, and observed variability from year to year in 

influenza A prevalence. This method resulted in estimates for each watershed for the seasonal 

median prevalence (with 95 percent credible intervals) expected during the fall, winter, and 

breeding periods, should influenza be introduced. Appendix 4 describes specific details about the 

model.

3) Estimation of Watershed Sample Sizes

Sample sizes required to identify influenza viruses of interest were developed for each season within

each priority watershed. Using the seasonally expected dabbling duck population (part 1) and the 

expected influenza prevalence (part 2) given introduction, we estimated the number of samples 

required to detect influenza viruses of interest for each season. We assumed that the influenza of 

interest represented 20 percent of the expected influenza viruses in a watershed, which is within 

the range of previously reported H5 prevalence values in dabbling ducks. This was reinforced during 

surveillance from 2015-2016, where mallards were the most commonly sampled dabbing duck 

species by far, and their Influenza A prevalence was 19.85%. This provided the data needed to 

compute detection prevalence thresholds.

The numbers in Table 1 represent the total annual number of samples [10] needed across 

watersheds to detect the presence of influenza A of interest at a 95 percent confidence level within 

a given watershed if the prevalence meets or exceeds the detection prevalence threshold. Detection 

prevalence thresholds for influenza viruses of interest vary among and within each sampled 

watershed by season, but have a minimum level of detection of 1%. The sample sizes also assume 

86.3 percent diagnostic sensitivity for the matrix rRT-PCR (Janice Pedersen, personal

communication; Mia Torchetti, personal communication). Sampling all watersheds requires 

extremely large sample numbers. In contrast, targeting sampling of high priority watersheds (Figure 

4) lowers the sample numbers required while still allowing us to infer distribution across the USA for 

influenza viruses of interest, because the targeting criteria preferentially selects watersheds with 

greatest connectivity and potential for spread (i.e., those most at risk for introduction of influenza 

viruses of interest). 
Surveillance Plan for HPAI in Wild Birds

10

Table 1. Estimated seasonal sample sizes needed by watershed ranking

Targeted Priority

Watersheds

No. of

Watersheds Sample Sizes1 Percent

Summer 58 7110 23%

Fall 114 12070 39%

Winter 77 11745 38%

All 

Seasons 122 30925 100%

Figure 4. Number of samples to collect annually by watershed and season for 

surveillance based on targeted watersheds

Summer

 

1 Sample sizes are computed at 95% confidence assuming a diagnostic test sensitivity of 86.3% and are a function of 

detection threshold with a minimum of 1% influenza A virus prevalence and estimated population sizes per watershed in 

each season. The range of detection level is based on the observed influenza A in the watersheds and assuming influenza A 

viruses of concern account for 20% of all influenza A viruses.
Surveillance Plan for HPAI in Wild Birds

11

Fall

Winter
Surveillance Plan for HPAI in Wild Birds

12

Sample Collection

National-level surveillance for HPAI in wild, migratory birds will follow a biological year beginning May 1, 

2017, through February 28, 2018. The sampling season dates defined in this document (summer, fall, 

winter) are purposely broad so that they can apply to all states, regardless of geographic location. 

Biologists can determine what time period within any given season is most appropriate for the 

waterfowl in their state. Summer samples however will ideally be focused on hatch-year-birds, since 

they are the population that is most likely to be infected. Samples should be collected when dabbling 

ducks or other species of interest are migrating into or through a specific state. Timing of seasonal 

migratory movements can vary widely depending on species, region, and current weather patterns. 

Sample collection efforts should be coordinated within each state and include efforts by federal, state, 

local, university, and non-governmental participants. Coordination will allow for efficient and costeffective collection of wild bird samples that are spread out in space and time. Local expertise should be 

utilized to attempt to collect informative samples from areas within targeted watersheds and species of 

high importance. 

This targeted sample sizes in this Surveillance Plan only apply to samples collected from apparently 

healthy wild birds (i.e. live-sampled wild birds or hunter harvest wild birds). Other collection strategies 

are identified in the Interagency Strategic Plan, including morbidity/mortality investigation (Appendix 5) 

and environmental sampling, but those strategies should be used when there is a specific situation that 

would require additional sampling above and beyond the healthy wild bird sampling detailed here. 

Examples would be morbidity/mortality sampling in response to an avian die-off, or environmental 

sampling near an area where a high pathogenic virus was detected. 

WS and its cooperators will continue to collect one cloacal and one oropharyngeal swab from each wild 

bird sampled by hunter harvest, sentinel, and live wild bird collection strategies. Cloacal and 

oropharyngeal swabs will be combined in the same tube of media per the AI Procedures Manual. The AI 

Procedures Manual provides details on sample collection, packaging, and shipping. All samples will be 

submitted to an approved National Animal Health Laboratory Network (NAHLN) laboratory. The NAHLN 

laboratory will screen samples to determine if influenza A virus is present; if influenza A is detected, the 

sample will be further analyzed by H5 and H7 specific assays. Samples with H5 or H7 detections at a 

NAHLN laboratory will be sent to the National Veterinary Services Laboratory (NVSL) for confirmatory 

testing and final diagnosis.
Surveillance Plan for HPAI in Wild Birds

13

Appendix 1. Wild Bird Species Confirmed Positive for HPAI in 

the United States (December 2014-February 2017)

1. American green-winged teal

2. American wigeon

3. Gadwall

4. Mallard

5. Northern pintail

6. Northern shoveler

7. Ring-necked duck

8. Wood duck

9. Canada goose

10. Lesser snow goose

11. Bald eagle

12. Cooper’s hawk

13. Peregrine falcon

14. Red-tailed hawk

15. Snowy owl

Appendix 2. Methods for Historical AI Clusters in Wild Birds 

Dabbler matrix infection data were stratified by date collected (Fall=August-December, 

Winter=December-February, Summer=May-August; time periods overlap because of variability across 

migrations) and related spatially on USGS 4-digit hydrologic unit code (HUC4). Clusters of matrix 

positive wild birds were identified using the Getis-Ord Gi* spatial statistic in ArcGIS. The analysis 

examines wild bird data (the number of samples and the AI status of each sample) that has been 

aggregated at the HUC4 watershed scale. Avian influenza activity within a watershed is analyzed in the 

context of neighboring watersheds. To be considered a statistically significant hotspot, the watershed 

will have a higher value and the surrounding watersheds will also have a higher value than would be 

expected due to chance.

We ran the analysis for both number of positive birds aggregated at the HUC4 watershed scale (count 

data) and on matrix prevalence aggregated at the HUC4 scales (proportion data). Watersheds identified 

as having a significant cluster of AI matrix positive dabblers or watersheds with a significant cluster of 

high matrix positive prevalence values were identified as high priority sample targets. Analyses used a 

previously calculated distance band of 100,000 meters [2]. 
Surveillance Plan for HPAI in Wild Birds

14

Appendix 3. Development of Watershed Level Consolidation Factor 

(Waterfowl Mixing) Metrics

Buhnerkempe et al. (2016) developed network metrics for network regions at a resolution of 200km [4]. 

To assign these metrics to watersheds, a method for disaggregation and assignment of the measures to 

watersheds was developed. The network nodes represent band recoveries for distinct square regions in 

North America extending 100km out from a central reference location. In order to disaggregate and 

assign measures to watersheds, the network measure of interest for each HUC4 watershed, CCCCii

HHHHHH4, 

were estimated using the method described in Equation 1 and Equation 2.

The network measure for three dabbling duck species (northern pintail [NOPI], American green-winged

teal [AGWT], and mallards [MALL]) is WWii

HHHHHHHH for each HUC4 watershed. This is the weighted sum of the 

network metric MMjjjj for HUC8 watershed j (within 100 km of the central reference location for network 

node k.) PPjjjj

HHHHHH8 is a weighting factor representing the 20-year mean proportion of dabbling duck band 

recoveries, njk, in HUC8 watershed j within the neighborhood of network node k. The CF was first 

assigned to the HUC8 watershed because the course spatial resolution of the HUC4 watersheds 

contained multiple network nodes. The HUC8 watersheds provided a resolution that allowed

representation of the neighborhoods of each network node (Figure 5).

Equation 1. Assignment of network measures to watersheds

WWii

HHHHHH4 = �MMjjjj

nn

ii=1

PPjjjj

HHHHHH8

Where, PPjjjj

HHHHHH8 = nnjjjj

∑ nnjjjj JJ

=ii,jj=1

To estimate the consolidation factor for dabbling ducks, we used an aggregate measure for mallards, 

American green-winged teal, and northern pintails. These three species are assumed to represent the 

majority of mixing and connectivity within North American waterfowl; thus, this aggregate metric can be 

considered a proxy for dabbling ducks. The aggregate measure assumed a linear relationship across the 

three species and was calculated as:

Equation 2. Aggregate measure for consolidation factor.

CCCCii

HHHHHH4 = �WWjjjj

MMMMMMMM

nn

ii=1

+ WWjjjj

AAAAAA + WWjjjjNNNNNNNN
Surveillance Plan for HPAI in Wild Birds

15

Figure 5. Example of the neighborhood for one network node (red dot and black line) using 

HUC8 watersheds. Colors represent the number of band recoveries in each watershed. Gray 

indicates no band recoveries.

This method of disaggregation allowed the identification and weighting of watersheds by their 

contribution to the CF. Figure 5 illustrates this point and represents the neighborhood of watersheds 

and the band recoveries that would have contributed to the network metric. There is obvious 

heterogeneity across the neighborhood from watershed to watershed. Figure 6 illustrates the HUC8 

level aggregate CF and the HUC4 aggregated CF. As presented in Figure 6, for some HUC4 watersheds 

only a small fraction of sub-watersheds (HUC8) were important for mixing. For this reason we only 

considered HUC4 watersheds that had a minimum proportion of 0.3 represented by HUC8 watersheds 

important for mixing in the ranking of watersheds for surveillance. We acknowledge that the minimum 

proportion of 0.3 is somewhat arbitrary, but it allows the identification of priority HUC4 watersheds. It 

also serves to address logistical considerations of allocating sampling to a HUC4 watershed with only a 

small proportion important for waterfowl mixing.
Surveillance Plan for HPAI in Wild Birds

16

Figure 6. Aggregate consolidation factor for HUC8 and HUC4 watersheds. Note some 

HUC8 watersheds with CF values only represent a small proportion of HUC4 

watersheds. 

Consolidation Factor for HUC8 Watersheds

Consolidation Factor for HUC4 Watersheds
Surveillance Plan for HPAI in Wild Birds

17

Appendix 4. Dabbling Duck Population Estimation and 

Temperature Constraints

Temperature constraints, the cumulative number of days above 0 ̊C and below 0 ̊C, were derived for 

each watershed in a given year from the National Oceanic and Atmospheric Administration (NOAA) 

weather station data. We identified weather stations within 250 km of each watershed centroid (up to 

10 closest stations), then calculated the number of days each station had an observed maximum 

temperature above 0 ̊C and the number of days with an observed minimum temperature below 0 ̊C. We 

then adjusted for the difference in elevation between each weather station and the watershed centroid 

using the average adiabatic lapse rate temperature correction formula [11]:

ΔT = 6.49 ̊C/1000 m

where ΔT represents a change in temperature of 6.49 ̊C for every 1,000 meters of elevation gained or 

lost between the weather station location and the watershed centroid. We averaged across the selected 

weather stations and over 30 years of observations, or all years in a 30-year time period for which data 

were available. The results of the temperature constraint are presented in 

Figure 7.

Figure 7. Watersheds identified with a 30-year median number of 365 days above 0 ̊C 

(yellow) and 180 days below 0 ̊C (blue).

Expected Dabbling Duck Populations

To estimate seasonal expected population that might be present in a watershed, we used two available 

datasets: bird band and recovery data and the estimated annual continental dabbling duck population 

available from the U.S. Fish and Wildlife Service from 1998 to 2012. These data were used to estimate 
Surveillance Plan for HPAI in Wild Birds

18

the expected proportion of the continental dabbling duck population present within each watershed in 

each season. This proportion was then used in concert with the estimated annual continental dabbling 

duck population to estimate the median population within each watershed. We used a Poisson-Gamma 

mixture model to model the median population in each watershed. The model structure for the 

population of dabbling ducks in HUC4 watershed j in time t and year k is estimated using the hierarchical 

model in Equation 3. The watershed population is modeled using the deterministic model in Equation 4. 

Equation 3.

�λjjjjjj, σσ2�bbjjjjjj,tt , θθ �

∝ PPPPPPPPPPPPPP�λjjjjjj�GGGGGGGGGG �λjjjjjj|

gg(bb,tt, θθ)

σσ2 ,

gg(bb,tt, θθ)

σσ2 � II IIIIII II �σσ2|.001, .001�

Where g(b,t, θ) is the expected proportion of the population in watershed j in time t and year k. This is 

estimated using the band recoveries for watershed j in month t and year k and the total recoveries for 

North America in month t and year k, �

bjtk

ttk

�. This proportion is multiplied by the estimated annual 

population of dabbling ducks θk. 

Equation 4.

gg(bb,tt, θθ) = �

bbjjjjjj

tt � θθ

Aggregating the data by month, year, and watershed allowed for including multiple forms of uncertainty 

in the estimates. These include but are not limited to variation in hunter effort, band reporting, 

recovery, and banding effort. While we did not model these explicitly, our intent is to provide an 

estimate of the expected population that may be present during the fall and winter periods. Our 

assumption is that these uncertainties do not greatly influence the median estimates for the population 

that might be present. However, this assumption might be violated in regions with low hunter effort or 

small populations. Our method also assumes that the band recoveries are proportional to the 

continental population at a given time and represent the spatial distribution of the continental 

population. This assumption may be violated early in the fall during the teal seasons, before the primary 

waterfowl seasons have opened. An example is if migration begins early and species such as mallards 

have begun to move prior to the season opening, which is typically in October. We have attempted to 

address this variation by including multiple years of data.

Expected Influenza A Prevalence

To estimate seasonal true expected prevalence within watersheds, we used prior dabbling duck 

surveillance data collected from 2006 to 2011 and from 2015-2017. The data was aggregated by month 

and year for each watershed, resulting in the count of sampled birds and the diagnostic test results. 

Aggregating the data by month, year, and watershed allowed for the inclusion of uncertainty in the 

estimates from differences in sampling effort and annual variation in prevalence. Our model structure 

used a binomial sampling distribution that accounted for uncertainty in the diagnostic test process. The 

expected seasonal prevalence was estimated using Equation 5.
Surveillance Plan for HPAI in Wild Birds

19

Equation 5.

�πjjjjjj, Se, Sp, θ �yyjjjjjj� ∝ BB BB( , gg�πjjjjjj�Se, Sp, θ�)Beta(SS |αα, ββ)Beta(SSSS|αα, ββ)BB (θθ|αα, ββ)

Equation 6.

gg(SS , SSSS, θθ) = θSe + (1 − θ)(1 − Sp)

Where n is the number of birds tested and gg(SS , SSSS, θθ) is the estimated true probability of an individual 

bird is infected with AI. θθ is the unknown prevalence of AI in the watershed in the month and year. Se 

and Sp are the sensitivity and specificity, respectively, of the diagnostic test. We used an uninformative 

Beta prior for the unknown prevalence:

θθ~BB (αα = 1, ββ = 1)

Uncertainty of sensitivity (Se) and specificity (Sp) was modeled using independent informative beta prior 

distributions [12] using known estimates for the diagnostic test Se (86.3%) and Sp (99.99%) (Janice 

Pedersen, personal communication; Mia Torchetti, personal communication). Specifically these priors 

were: 

SS ~BB (αα = 20.833, ββ = 4.148)

SSSS~BB (αα = 8.403, ββ = 1.001)

Model Fitting and Evaluation

The models for expected population and prevalence were fit for each watershed and biological season 

using Markov chain Monte Carlo (MCMC) techniques and implemented in WinBUGS software [13]. 

Posterior inferences were based on 100,000 iterations with a sampling lag of 5, after a burn-in of 20,000 

iterations was discarded. We assessed convergence by running five chains from dispersed starting 

values, observing autocorrelation among samples and investigating the Brooks-Gelman-Rubin 

convergence statistic [14]. We used the median of the posterior distributions as an estimate for the 

parameters of interest, and the 2.5 and 97.5 percent points as estimates of the 95 percent credible 

intervals.
Surveillance Plan for HPAI in Wild Birds

20

Appendix 5. Morbidity and Mortality Surveillance

The technical guidance in this plan has focused on active surveillance of live or hunter-harvested 

waterfowl species to achieve the stated surveillance objectives. However, passive surveillance through 

investigation of morbidity and mortality events in wild birds can provide an ancillary source of 

information or surveillance stream for detection of influenza viruses of interest. Within a given HUC, a 

sample collected from surveillance of morbidity and mortality events should provide at least as much 

information regarding the detection of novel influenza viruses of interest as a sample collected from 

hunter harvested or live-bird surveillance. Thus, samples collected during passive surveillance can 

potentially be used to achieve active surveillance sample size goals. Additionally, if individuals involved 

in a morbidity or mortality event have a higher probability of being infected with influenza viruses of 

interest, including samples from these events may increase the overall probability of detection of the 

surveillance program. These types of differential probabilities of infection between apparently healthy 

and sick individuals have been exploited for other wildlife diseases to maximize the efficiency of 

detection surveillance efforts [15]. However, passive surveillance is only useful when the goal of a 

surveillance program is detection of a pathogen. It should not be used when intensity metrics (e.g., 

prevalence) are the focus. 

Including morbidity and mortality surveillance directly in the statistical design of this technical plan is 

not feasible due to the high stochastic nature in the occurrence and location of these events and the 

resulting lack of a useful underlying probability model. Therefore, we recommend investigating all 

significant morbidity and mortality events, particularly if they occur in regions important to species 

conservation or domestic poultry production. These passive surveillance efforts should focus on wild 

bird species known or suspected to be susceptible to infection by influenza viruses of interest. 

Preliminary information from the current outbreak suggests that Canada geese and raptor species likely 

to feed or scavenge on waterfowl may be particularly affected, and therefore should be included in any 

passive surveillance activities. Additionally, morbidity and mortality events of other groups of birds 

known to be affected or carry influenza viruses such as waterfowl, wading birds, geese, and swans all 

represent potential sources of information regarding the geographic and host range expansion of novel 

influenza viruses.

We recommend conducting passive surveillance as a complementary effort to the active surveillance 

activities. The integration of these two efforts will provide the highest likelihood of early detection of 

influenza viruses of interest in new geographic locations and wildlife species. 
Surveillance Plan for HPAI in Wild Birds

21

Appendix 6. Wildlife Conservation Concerns

The statistical design of this surveillance plan has drawn from current knowledge about the biology and 

movements of focal waterfowl species (e.g., dabbling ducks) as well as current knowledge about 

distribution and diversity of influenza viruses within those species to target specific geographic regions 

for surveillance. This has provided a comprehensive strategy for maximizing the likelihood of detecting 

influenza viruses of interest in waterfowl and establishing key locations for monitoring the intensity of 

infection post-detection. However, given the breadth of movements of migratory waterfowl, they can 

potentially carry and spread influenza viruses of concern into locations that are home to species of high 

conservation value and negatively impact their resident populations. Therefore, in addition to the 

geographic regions selected for surveillance described previously, specific locations within these regions 

or additional regions with significant species conservation value should be incorporated into the 

implementation of this surveillance design.

To identify these specific areas of high conservation value, we recommend consulting with tribal, State,

and Federal wildlife biologists with specific knowledge of State or Federal species of conservation 

concern to identify any deficiencies of the surveillance program’s current geographic coverage. Such 

deficiencies should then be remedied prior to final implementation of the surveillance program. Specific 

groups of conservation species to target are wetland dependent species, wading bird species, 

gallinaceous birds, and raptors that potentially feed or scavenge on waterfowl. Examples of such 

species that may be at risk include: 

• Whooping cranes (Grus americana): wetland dependent ; maintain specific migratory routes and 

stop-over sites to target for surveillance; 

• Piping plovers (Charadrius melodus): wading birds that rely solely on gravel or sandy shoals for 

nesting and make large migratory movements; 

• Attwater’s prairie chicken (Tympanuchus cupido attwateri), lesser prairie chicken (Tympanchus 

pallidicinctus), and Gunnsion sage grouse (Centrocercus minimus): gallinaceous conservation 

species that likely have a low risk of influenza infection, but may be susceptible if waterfowl 

stop-over sites are also used by these species; 

• California condors (Gymnogyps californianus) and peregrine falcons (Falco peregrinus): raptor 

species of high conservation value that potentially feed or scavenge waterfowl where their 

distributions overlap. 

These examples are not exhaustive, but illustrate the important need to address conservation concerns 

during surveillance activities.

We recommend adding identified areas of conservation concern to the geographic sampling frame if 

they are not already included within a selected HUC; or, if they are already included in the selection, 

targeting these locations for focused sampling within the HUC. Including these high-value areas in the 

surveillance for influenza viruses of interest will increase the probability of early detection of the 

pathogen in populations of these critical species, and help mitigate negative impacts while management 

actions are still feasible. 
Surveillance Plan for HPAI in Wild Birds

22

Appendix 7. Poultry Production Regions

Livestock distribution in the United States (U.S.) is typically mapped at a county-level or larger 

resolution. This results in conflicts of scale when using farm production data with other data sources 

such as waterfowl band recovery data. To address this problem we used the Farm Location and 

Agricultural Production Simulator (FLAPS) that simulates the distribution and populations of individual 

livestock farms throughout the conterminous U.S. [5]. FLAPS uses iterative proportional-fitting 

algorithms for the hierarchical structure of the U.S. Census of Agriculture and imputes unpublished state 

and county-level livestock population totals that are redacted to ensure confidentiality. The FLAPS 

model is based on a data representing the presence and absence of farms and is used to develop a 

national-scale distribution model that predicts the distribution of individual farms at a 100 m resolution. 

Microsimulation algorithms are used to simulate the populations and locations of individual farms using 

output from the imputed Census of Agriculture dataset and distribution model. The model is validated 

using aerial photography that confirms the presence or absence of livestock farms at 10,238 locations 

across the U.S. The model has demonstrated good predictive capacity using cross-validation methods. 

Verification of the model shows that FLAPS accurately imputes and simulates Census of Agriculture data 

based on absolute percent difference value of < 0.01% at the state-to-national scale and 0.03% for the 

individual farm-to-county scale. The FLAPS model and data is freely available at 

http://flaps.biology.colostate.edu. The watershed level distribution of poultry farms and inventory 

generated by FLAPS used in this plan is illustrated in Figure 8. These data were used to identify 

watersheds with an estimated annual average inventory greater than 10 million birds. 
Surveillance Plan for HPAI in Wild Birds

23

Figure 8. Distribution of U.S. poultry production in the United States visualized by the 

Farm Location and Agricultural Production Simulator (FLAPS). (Top) Number of 

poultry farms per watershed; (Bottom) Mean number of birds per watershed. Red 

outline indicates watersheds with an annual average inventory greater than 10 

million birds based on the 2012 NASS census.
Surveillance Plan for HPAI in Wild Birds

24

References

1. United States Geological Survey. Watershed Boundary Dataset. cited March 1, 2015. 

http://nhd.usgs.gov/wbd.html

2. Bevins SN, Pedersen K, Lutman MW, Baroch JA, Schmit BS, et al. (2014) Large-scale avian influenza 

surveillance in wild birds throughout the United States. PLoS ONE: e104360.

3. Farnsworth ML, Miller RS, Pedersen K, Lutman MW, Swafford SR, et al. (2012) Environmental and 

demographic determinants of avian influenza viruses in waterfowl across the contiguous United 

States. PLoS ONE: e32729.

4. Buhnerkempe MG, Webb CT, Merton AA, Buhnerkempe JE, Givens GH, et al. (2016) Identification of 

migratory bird flyways in North America using community detection on biological networks. 

Ecological Applications 26: 740-751.

5. Burdett CL, Kraus BR, Garza SJ, Miller RS, Bjork KE (2015) Simulating the distribution of individual 

livestock farms and their populations in the United States: an example using domestic swine

(Sus scrofa domesticus) farms. PLoS ONE: e0140338.

6. Brown JD, Goekjian G, Poulson R, Valeika S, Stallknecht DE (2009) Avian influenza virus in water: 

Infectivity is dependent on pH, salinity and temperature. Veterinary Microbiology 136: 20-26.

7. Schummer ML, Kaminski RM, Raedeke AH, Graber DA (2009) Weather-related indices of autumn–

winter dabbling duck abundance in middle North America. Journal of Wildlife Management 74: 

94-101.

8. Baldassarre GA, Bolen EG Waterfowl (1994) ecology and management. Wiley, 567 pp.

9. United States Geologic Survey Patuxent Wildlife Research Center. North American Breeding Bird 

Survey. cited December 14, 2012. https://www.pwrc.usgs.gov/bbs/

10. Cannon RM (2001) Sense and sensitivity - designing surveys based on an imperfect test. Preventive 

Veterinary Medicine. 49: 141-163.

11. United States National Oceanic and Atmospheric Administration. National Climate Data. cited April 

27, 2011. http://www.ncdc.noaa.gov/

12. Hanson T, Johnson W, Gardner I (2003) Hierarchical models for estimating herd prevalence and test 

accuracy in the absence of a gold standard. Journal of Agricultural, Biological, and Environmental 

Statistics 8: 223-239.

13. Lunn D, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS - A Bayesian modelling framework: 

Concepts, structure, and extensibility. Statistics and Computing 10: 325-337.

14. Brooks SP, Gelman A (1998) General methods for monitoring convergence of iterative simulations. 

Journal of Computational and Graphical Statistics 7: 434-455.

15. Walsh DP, Miller MW (2010) A weighted surveillance approach for detecting chronic wasting disease 

foci. Journal of Wildlife Diseases 46: 118-135.